Abstract
The heat affected zone and arc length parameters have a vital role to play in determining the integrity of a weld structure. The cooks distance is a statistical diagnostic employed in this study to select the best optimum combination of welding process parameters. Mild steel plate was the choice material used to produce the weld specimen, which was welded with the Tungsten inert gas method. The RSM model was used to develop an optimal solution that can explain the behavior of the welded joint with respect to the heat affected zone and arc length, different diagnostic techniques were employed which includes the normal probability plot and cooks distance plot. The model developed has sufficient merit as the results obtained shows that the cooks distance values is within the range of 0 and 1 indicating the absence of outlier in the data making the optimal solution highly acceptable.
Highlights
Tungsten inert gas welding (TIG) popularly known for its production of excellent quality welds having minimum fumes, slags and porosity [1].Gas tungsten arc welding process is applicable to different steel materials; most metal fabrication workers prefer this technique because of its cost effectiveness [2].During welding the solid metal is transformed into a molten state by the application of heat, which cools off and solidifies
Optimization processes today have integrated some statistical techniques to increase the reliability of the optimal solution, the cooks distance is one of the statistical diagnostics employed
In developing a regression model,the data has to be free from outliers known as erroneous data, the presence of outliers has an influence on the accuracy and reliability of the model [6].The kernel density estimation is nonparametric technique required for data smoothening which behaves similar to the cooks distance [7], and later suggested a type of Cook’s distance in local polynomial regression [8].The exact distribution of Cook’s distance was used to estimate the error found in a multivariate regression analysis
Summary
Tungsten inert gas welding (TIG) popularly known for its production of excellent quality welds having minimum fumes, slags and porosity [1].Gas tungsten arc welding process is applicable to different steel materials; most metal fabrication workers prefer this technique because of its cost effectiveness [2].During welding the solid metal is transformed into a molten state by the application of heat, which cools off and solidifies. To get the most suitable heat input that will melt the solid metal and filler wire effectively, the technique of optimization is required to select and control the welding process parameters [3]. Having knowledge of the arc length and heat affected zone of the welding process and combining these parameters optimally can be used to produce very reliable numerical models to help predict the output quality of mild steel fabricated structures. In developing a regression model ,the data has to be free from outliers known as erroneous data, the presence of outliers has an influence on the accuracy and reliability of the model [6].The kernel density estimation is nonparametric technique required for data smoothening which behaves similar to the cooks distance [7], and later suggested a type of Cook’s distance in local polynomial regression [8].The exact distribution of Cook’s distance was used to estimate the error found in a multivariate regression analysis. The results obtained was compared to the traditional rule of thumb technique which shows better scientific and systematic properties [9]
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